Chapter 10 Dynamics, growth and geography

Long term equilibrium adjustment: dλ i /λ i = η(w i – ω) Value of η sets the speed of adjustment but in general does not have an effect on the outcome Exceptions: – overshooting – unstable equilibrium – not the “nearest” equilibrium is reached

Figure 10.1 Regular adjustment dynamics

Figure 10.2 Special adjustment dynamics (along vertical axis, ; horizontal axis, number of reallocations)

Figure 10.2 Special adjustment dynamics (along vertical axis, ; horizontal axis, number of reallocations)

Figure 10.2 Special adjustment dynamics (along vertical axis, ; horizontal axis, number of reallocations)

Can growth periods be simulated? Convergence/divergence Different for countries/regions Convergence/divergence speed different in different periods

Figure 10.3 Histogram of per capita income, selected years

Figure 10.4 Regional convergence in the EU, speed of convergence estimates

Figure 10.5 Regional income inequality in the EU: Lorenz curves

EU

Between-country inequality –Assuming equal gdp/cap inside each country Within-country inequality –Assuming equal national gdp/cap worldwide

Figure 10.6 Regional income inequality in the EU: Theil index and Gini coefficient

Figure 10.7 Leaders and laggards in the world economy, income per capita (% of world average) year Italy Iraq Iran Netherlands UK Australia USA Switzerland India China oAfrica W Offshoots New Zealand Australia Many Italy

To be explained 1.For almost all countries even increasing level of income 2.Differences between countries may persist for a long time 3.Long periods of stagnation can be followed by long periodes of growth 4.Frequent changes in economic ranking (leap- frogging)

Theories Endogenous growth Y = A f (K,L) Total factor productivity A as a function of K or L can explain (1) In a closed model A can be structurally different per country: can explain (2) (3) and (4) cannot be explained by endogenous growth theory Need for geographical economics

T 0 1 0,5 λ1λ1 Fig 4.10 The bell-shaped curve Unstable equilibria Stable equilibria VL model: with lowering T from dispersion to agglomeration to dispersion Very simple explanation of (3) Recall Krugman & Venables (1995)

Simulations of (3) and (4) Using the 24 region racetrack model with congestion, unchanged ε=5, δ=0.6, τ=0.05 Simulating a change in transport costs over time Some random initial distribution (history) Find long term equilibirum with T =3, then decreasing Herfindahl index H=Σλ i 2

Figure 10.8 Distribution of manufacturing and Herfindahl index

Figure 10.9 Evolution of agglomeration, the Herfindahl index

Figure Several phases of the reallocation process

H does not tell anything about “spikes”

Figure Dynamics of regional size; regions 3, 6, and 9

Combine agglomeration and growth Baldwin & Forslid (2000) Extend the CP model with capital K produced by sector In (investment sector) With global knowledge spill-overs location of In does not matter With local knowledge spill-overs location of In does matter High policy relevance: many governments stimulate knowledge flows to periphery with universities/high-tech industrial parks etc.

Baldwin & Martin (2004) Cost function M sector: R + Wβx i K is produced under perfect competition with only variable labor α I under knowledge spill-overs: α I falls with rising output Q k =L I / α I α I = 1 / [ K -1 + κ K* -1 ] With Q k = flow of new capital L I = employment in investment sector K = stock of knowledge (*= other region) κ = parameter degree of spillovers (capital depreciates in one period)

Intertemporal utility U = Σ t (1/1+θ) t [ln (F t 1-δ M t δ )] See box 10.1 Mobility related to difference in present value of real wages in each region

Main results For two region model only spreading and complete agglomeration into one region are stable long-term equilibria -> same as in CP model with increasing κ more stable equilibria possible

Figure Stability in the Baldwin-Forslid economic growth model

"deep determinants of growth" growth different because A is localized, but: why is A localized? institutions (table 10.4) relevant again the discussion on first nature returns –climate, land-locked –tropical diseases (Sachs) missing: second nature: the role of geography relative to other geographies –spatial autocorrelation (first block of the course by Paul Elhorst) –spatial autocorrelation of institutions?

Figure Scatterplot of own and neighboring institutions

Conclusions (p451) Integrated endogenous growth/geographical economic models can deal with (1)-(4) but –do not pay attention to deep determinants of differentiated growth models that do take account of deep determinants ignore second nature/spatial interdependence long term history and path-dependence (box 10.2) –country borders change over time –cities do not: research more promising needed: integration of deep determinants, history and second nature geography